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Age- and Influenza Activity-Stratified Case Definitions of Influenza-Like Illness: Experience from Hospital-Based Influenza Surveillance in South Korea

Authors
Yang, Tae UnCheong, Hee JinSong, Joon YoungLee, Jin SooWie, Seong-HeonKim, Young KeunChoi, Won SukLee, JacobJeong, Hye WonKim, Woo Joo
Issue Date
24-Jan-2014
Publisher
PUBLIC LIBRARY SCIENCE
Citation
PLOS ONE, v.9, no.1
Indexed
SCIE
SCOPUS
Journal Title
PLOS ONE
Volume
9
Number
1
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/99508
DOI
10.1371/journal.pone.0084873
ISSN
1932-6203
Abstract
Objectives: This study aims to identify clinical case definitions of influenza with higher accuracy in patients stratified by age group and influenza activity using hospital-based surveillance system. Methods: In seven tertiary hospitals across South Korea during 2011-2012 influenza season, respiratory specimens were obtained from patients presenting an influenza-like illness (ILI), defined as having fever plus at least one of following symptoms: cough, sore throat or rhinorrhea. Influenza was confirmed by reverse transcriptase-polymerase chain reaction. We performed multivariate logistic regression analyses to identify clinical variables with better relation with laboratory-confirmed influenza, and compared the accuracy of combinations. Results: Over the study period, we enrolled 1417 patients, of which 647 had laboratory-confirmed influenza. Patients with cough, rhinorrhea, sore throat or headache were more likely to have influenza (p<0.05). The most accurate criterion across the study population was the combination of cough, rhinorrhea, sore throat and headache (sensitivity 71.3%, specificity 60.1% and AUROC 0.66). The combination of rhinorrhea, sore throat and sputum during the peak influenza activity period in the young age group showed higher accuracy than that using the whole population (sensitivity 89.3%, specificity 72.1%, and AUROC 0.81). Conclusions: The accuracy of clinical case definitions of influenza differed across age groups and influenza activity periods. Categorizing the entire population into subgroups would improve the detection of influenza patients in the hospital-based surveillance system.
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